Please use this identifier to cite or link to this item:
|Title:||Detection of failures in civil structures using artificial neural networks||Authors:||Lim, Z.W.
|Issue Date:||2009||Citation:||Lim, Z.W.,Tan, C.K.-Y.,Seah, W.K.-G.,Tan, G.-H. (2009). Detection of failures in civil structures using artificial neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5769 LNCS (PART 2) : 976-985. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-04277-5_98||Abstract:||This paper presents an approach to failure detection in civil structure using supervised learning of data under normal conditions. For supervised learning to work, we would typically need data of anomalous cases and normal conditions. However, in reality there is abundant of data under normal conditions, and little or none anomalous data. Anomalous data can be generated from simulation using finite element modeling (FEM). However, every structure needs a specific FEM, and simulation may not cover all damage scenarios. Thus, we propose supervised learning of normal strain data using artificial neural networks and make prediction of the strain at future time instances. Large prediction error indicates anomalies in the structure. We also explore learning of both temporal trends and relationship of nearby sensors. Most literature in anomalies detection makes use of either temporal information or relationship between sensors, and we show that it is advantageous to use both. © 2009 Springer Berlin Heidelberg.||Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)||URI:||http://scholarbank.nus.edu.sg/handle/10635/40752||ISBN:||3642042767||ISSN:||03029743||DOI:||10.1007/978-3-642-04277-5_98|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 21, 2020
checked on Dec 30, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.